A method to identify and quantify the complete peptide composition in protein hydrolysates

Vreeke, Gijs J.C.; Lubbers, Wouter D.; Vincken, Jean‐Paul; Wierenga, Peter A.

doi:10.1016/j.aca.2022.339616

Cited by 19 publications

(9 citation statements)

References 35 publications

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Section: Resultssupporting

confidence: 79%

“…These factors illustrate why more hydrophobic peptides might generally have a better ionization efficiency in gradient RP-HPLC. Other studies have found a direct empirical correlation between ionization efficiency and retention times, corroborating these findings [50,52,53]. found that in complex mixtures, there is a weak non-linear relationship between ionization efficiency and hydrophobicity, which they argue might be linear in a simpler mixture [58].…”

Section: Representative Model 1: Bulky Hydrophobic and Aromatic Amino...supporting

confidence: 54%

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Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS proteomics: A deep learning approach

Abdul-Khalek

Wimmer

Overgaard

et al. 2023

Preprint

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Accurate and absolute quantification of individual peptides in complex mixtures is a challenge not easily overcome. A potential solution is the use of quantitative mass spectrometry (MS) based methods, however, current state of the art requires foreground knowledge and isotopically labeled standards for each peptide to be accurately quantified. This increases analytical expenses, time consumption, and labor, limiting the number of peptides that can be quantified. A key step in developing less restrictive label-free quantitative peptidomics methods is understanding of the physicochemical properties of peptides that influence the MS response. In this work, a deep learning model was developed to identify the most relevant physicochemical properties based on repository MS data from equimolar peptide pools. Using an autoencoder with attention mechanism and correlating attention weights with corresponding physicochemical property indices from AAindex1, we were able to obtain insight on the properties governing the peptide-level MS1 response. These properties can be grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, we present a model for predicting the MS1 intensity output based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 11%.

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Section: Resultssupporting

confidence: 79%

Section: Representative Model 1: Bulky Hydrophobic and Aromatic Amino...supporting

confidence: 54%

Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS proteomics: A deep learning approach

Abdul-Khalek

Wimmer

Overgaard

et al. 2023

Preprint

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show abstract

“…The hypothesis is further corroborated by previous studies, where a positive correlation between ionization efficiency and retention times was empirically demonstrated. [38][39][40]. Nevertheless, hydrophobic stretches have also been linked with difficulty of synthesis [41], which would result in deletion isoforms of the intended peptide, and thus lower abundance and ultimately response in an intended equimolar synthetic peptide library.…”

Section: Representative Model 2: Aromatic Aas Followed By a Hydrophob...mentioning

confidence: 99%

Decoding the Impact of Neighboring Amino Acid on ESI-MS Intensity Output through Deep Learning

Abdul-Khalek,

Wimmer,

Overgaard

et al. 2024

Preprint

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Peptide-level quantification using mass spectrometry (MS) is no trivial task as the physicochemical properties affect both response and detectability. The specific amino acid (AA) sequence affects these properties, however the link from sequence to intensity output remains poorly understood. In this work, we explore combinations of amino acid pairs (i.e., dimer motifs) to determine a potential relationship between the local amino acid environment and MS1 intensity. For this purpose, a deep learning (DL) model, consisting of an encoder-decoder with an attention mechanism, was built. The attention mechanism allowed to identify the most relevant motifs. Specific patterns were consistently observed where a bulky/aromatic and hydrophobic AA followed by a cationic AA as well as consecutive bulky/aromatic and hydrophobic AAs were found important for the MS1 intensity. Correlating attention weights to mean MS1 intensities revealed that some important motifs, particularly containing Trp, His, and Cys, were linked with low responding peptides whereas motifs containing Lys and most bulky hydrophobic AAs were often associated with high responding peptides. Moreover, Asn-Gly was associated with low MS1 response. The model could predict MS1 response with a mean average percentage error of ~11% and a Pearson correlation coefficient of ~0.68.

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“…Peptidomic or proteomic approaches are involved in diverse areas of food analyses, concerning issues such as the authenticity and origin of food products, changes taking place during food processing or storage [189][190][191], analysis of food protein hydrolysates [192], the biological activity of peptides contained in foods (milk, eggs) or raw materials [193][194][195][196] or factors influencing proteolysis of human milk proteins [197].…”

Section: Food Analysismentioning

confidence: 99%

Recent developments and applications of capillary and microchip electrophoresis in proteomics and peptidomics (mid‐2018–2022)

Štěpánová

Kašička

2023

J of Separation Science

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This review gives a wide overview of recent advances and applications of capillary electrophoresis and microchip capillary electrophoresis methods in the fields of proteomics and peptidomics in the period from mid‐2018 up to the end of 2022. The methodological topics covering sample preparation and concentration techniques, hyphenation of capillary electrophoresis methods with mass spectrometry, and multidimensional separations by on‐line or off‐line coupled different capillary electrophoresis and liquid chromatography techniques are described and new developments in both bottom‐up and top‐down approaches in proteomics are presented. In addition, various applications of capillary electrophoresis methods in proteomic and peptidomic studies are demonstrated. They include monitoring of protein posttranslational modifications and applications in biological and biochemical research, clinical peptidomics and proteomics, and food analysis.

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A method to identify and quantify the complete peptide composition in protein hydrolysates

Cited by 19 publications

References 35 publications

Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS proteomics: A deep learning approach

Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS proteomics: A deep learning approach

Decoding the Impact of Neighboring Amino Acid on ESI-MS Intensity Output through Deep Learning

Recent developments and applications of capillary and microchip electrophoresis in proteomics and peptidomics (mid‐2018–2022)

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